ValueError: mutable default for field common is not allowed: use default_factory

I am getting this error when initializing the model. This is the traceback: `--------------------------------------------------------------------------- ValueError Traceback (most recent call last) Cell In[16], line 1 ----> 1 e = XlitEngine(src_script_type="indic", beam_width=4, rescore=True)

File \Lib\site-packages\ai4bharat\transliteration\xlit_src.py:12, in XlitEngine(lang2use, beam_width, rescore, model_type, src_script_type) 10 elif src_script_type == "indic": 11 from .transformer import XlitEngineTransformer_Indic2En ---> 12 return XlitEngineTransformer_Indic2En(beam_width=beam_width, rescore=rescore) 14 elif model_type == "rnn": 15 assert src_script_type in {"roman", "latin", "en"}

File \Lib\site-packages\ai4bharat\transliteration\transformer\indic2en.py:60, in XlitEngineTransformer_Indic2En.init(self, beam_width, rescore) 57 else: 58 dicts_folder = None ---> 60 super().init(models_path, beam_width=beam_width, rescore=rescore)

File \Lib\site-packages\ai4bharat\transliteration\transformer\base_engine.py:45, in BaseEngineTransformer.init(self, models_path, beam_width, rescore) 42 lang_pairs_csv = ','.join(["en-"+lang for lang in self.all_supported_langs]) 44 # initialize the model ---> 45 from .custom_interactive import Transliterator 46 self.transliterator = Transliterator( 47 os.path.join(models_path, CHARS_FOLDER), 48 os.path.join(models_path, MODEL_FILE), (...) 51 beam = beam_width, batch_size = 32, 52 ) 54 self.beam_width = beam_width

File \Lib\site-packages\ai4bharat\transliteration\transformer\custom_interactive.py:23 20 import numpy as np 21 import torch ---> 23 from fairseq import checkpoint_utils, distributed_utils, options, tasks, utils 24 from fairseq.dataclass.configs import FairseqConfig 25 from fairseq.dataclass.utils import convert_namespace_to_omegaconf

File \Lib\site-packages\fairseq__init.py:20 17 all__ = ["pdb"] 19 # backwards compatibility to support from fairseq.X import Y ---> 20 from fairseq.distributed import utils as distributed_utils 21 from fairseq.logging import meters, metrics, progress_bar # noqa 23 sys.modules["fairseq.distributed_utils"] = distributed_utils

File \Lib\site-packages\fairseq\distributed__init__.py:7 1 # Copyright (c) Facebook, Inc. and its affiliates. 2 # 3 # This source code is licensed under the MIT license found in the 4 # LICENSE file in the root directory of this source tree. 6 from .distributed_timeout_wrapper import DistributedTimeoutWrapper ----> 7 from .fully_sharded_data_parallel import ( 8 fsdp_enable_wrap, 9 fsdp_wrap, 10 FullyShardedDataParallel, 11 ) 12 from .legacy_distributed_data_parallel import LegacyDistributedDataParallel 13 from .module_proxy_wrapper import ModuleProxyWrapper

File \Lib\site-packages\fairseq\distributed\fully_sharded_data_parallel.py:10 7 from typing import Optional 9 import torch ---> 10 from fairseq.dataclass.configs import DistributedTrainingConfig 11 from fairseq.distributed import utils as dist_utils 14 try:

File \Lib\site-packages\fairseq\dataclass__init.py:6 1 # Copyright (c) Facebook, Inc. and its affiliates. 2 # 3 # This source code is licensed under the MIT license found in the 4 # LICENSE file in the root directory of this source tree. ----> 6 from .configs import FairseqDataclass 7 from .constants import ChoiceEnum 10 all__ = [ 11 "FairseqDataclass", 12 "ChoiceEnum", 13 ]

File \Lib\site-packages\fairseq\dataclass\configs.py:1104 1095 ema_update_freq: int = field( 1096 default=1, metadata={"help": "Do EMA update every this many model updates"} 1097 ) 1098 ema_fp32: bool = field( 1099 default=False, 1100 metadata={"help": "If true, store EMA model in fp32 even if model is in fp16"}, 1101 ) -> 1104 @dataclass 1105 class FairseqConfig(FairseqDataclass): 1106 common: CommonConfig = CommonConfig() 1107 common_eval: CommonEvalConfig = CommonEvalConfig()

File \Lib\dataclasses.py:1230, in dataclass(cls, init, repr, eq, order, unsafe_hash, frozen, match_args, kw_only, slots, weakref_slot) 1227 return wrap 1229 # We're called as @dataclass without parens. -> 1230 return wrap(cls)

File \Lib\dataclasses.py:1220, in dataclass..wrap(cls) 1219 def wrap(cls): -> 1220 return _process_class(cls, init, repr, eq, order, unsafe_hash, 1221 frozen, match_args, kw_only, slots, 1222 weakref_slot)

File \Lib\dataclasses.py:958, in _process_class(cls, init, repr, eq, order, unsafe_hash, frozen, match_args, kw_only, slots, weakref_slot) 955 kw_only = True 956 else: 957 # Otherwise it's a field of some type. --> 958 cls_fields.append(_get_field(cls, name, type, kw_only)) 960 for f in cls_fields: 961 fields[f.name] = f

File \Lib\dataclasses.py:815, in _get_field(cls, a_name, a_type, default_kw_only) 811 # For real fields, disallow mutable defaults. Use unhashable as a proxy 812 # indicator for mutability. Read the hash attribute from the class, 813 # not the instance. 814 if f._field_type is _FIELD and f.default.class.hash is None: --> 815 raise ValueError(f'mutable default {type(f.default)} for field ' 816 f'{f.name} is not allowed: use default_factory') 818 return f

ValueError: mutable default for field common is not allowed: use default_factory `

AI4Bharat / IndicXlit

ValueError: mutable default for field common is not allowed: use default_factory #24